Instructions to use zeyuren2002/EvalMDE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zeyuren2002/EvalMDE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zeyuren2002/EvalMDE", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| import logging | |
| import sys | |
| def setup_logging(args=None, log_level=None, reset=False): | |
| if logging.root.handlers: | |
| if reset: | |
| for handler in logging.root.handlers[:]: | |
| logging.root.removeHandler(handler) | |
| else: | |
| return | |
| if log_level is None and args is not None: | |
| log_level = getattr(args, "console_log_level", None) | |
| if log_level is None: | |
| log_level = "INFO" | |
| log_level = getattr(logging, str(log_level).upper()) | |
| handler = logging.StreamHandler(sys.stdout) | |
| handler.setFormatter(logging.Formatter("%(message)s")) | |
| logging.root.setLevel(log_level) | |
| logging.root.addHandler(handler) | |
| setup_logging() | |